1. Related Applications
The present application claims priority to Chinese Patent Application Number 200610149452.1, filed Nov. 17, 2006, the entirety of which is hereby incorporated by reference.
2. Field of the Invention
The invention relates to an object classifier, and more particularly, to a method and apparatus for determining a classification boundary between an object and a background when the object classifier is used to classify and recognize the object.
3. Technical Background
In the pattern recognition of an object image, various classifiers, such as SVM (Support Vector Machines) classifiers, neural network classifiers and so on, are widely employed to classify and recognize objects like vehicles, motorcycles, or pedestrians.
In the prior art, vehicles are often recognized using an SVM classifier when performing pattern recognition of a vehicle in an image. As shown in FIG. 1, classification and recognition of a vehicle using an SVM classifier typically involves two processes: a training process and a recognition process.
During the training process, the SVM classifier is trained by manually selecting images as vehicle training samples (S1) and background training samples (S2), and using the training samples to train the SVM classifier to recognize a vehicle (S3). Once the training process is completed (S4), the recognition process begins.
In the recognition process, first a ROI (Region of Interest) is extracted from the image (S5) using a knowledge-based Method (for example, under-shadow, horizontal edge, vertical edge, symmetry and so on). Next, the extracted ROI is classified and recognized by the trained SVM classifier and a confidence value is obtained for the ROI (S6). Finally, a determination is made (S7) as to whether a vehicle or a background is contained in the image, based on the confidence value for the ROI.
Additionally, when the trained SVM classifier is used to classify and recognize the vehicle and background training samples, a confidence value may be obtained for each of the training samples. The histogram distribution of the confidence values is shown in FIG. 2. Through x2-testing, the confidence values are demonstrated to fit a normal distribution. The confidence values may then be used to obtain confidence probability density distribution curves for the vehicle and background training samples through curve fitting. FIG. 3 illustrates a sample probability density distribution for the vehicle and background training samples. As shown in FIG. 3, the confidence values for the vehicle and the confidence values for the background are completely separated. That is, the confidence values for the vehicle are all greater than zero and the confidence values for the background are all less than zero. In this scenario, because the confidence values for the vehicle and the background are completely separated, a determination may be made as to whether a vehicle or a background is contained in the ROI based on whether the confidence value is greater than zero. When the confidence value is greater than zero, the ROI contains a vehicle. When the confidence value is less than zero, the ROI contains a background.
In order for an SVM classifier to correctly recognize vehicles or backgrounds from an image, it must be calibrated using a sufficient number of training samples to address the potential variations of the vehicle, or the vehicle environment that the SVM classifier may encounter. However, in practical application the sample distribution should be very broad in order for the system to account for the potential variations in the vehicle and the vehicle environment in the recognition process. For example, the vehicles in an image may vary in type and color. The vehicles may also be traveling in different directions or located at various distances and angles with respect to the vehicle on which the classifier is located. Furthermore, the vehicle environment, such as light level, road conditions, weather conditions, and the background may vary dramatically. Because the potential variations are so numerous, a SVM classifier may incorrectly recognize vehicles and backgrounds. In other words, in the prior art it is difficult for a SVM classifier to recognize a vehicle as a background or vice versa because the SVM may not be adequately trained.
When an SVM is not adequately trained as described above, the confidence probability density distribution curves corresponding to the vehicle and the background partially overlap, as shown in FIG. 4.
If the confidence probability density distribution curves overlap and a fixed confidence value is used as the classification boundary in the recognition process (the confidence value is usually zero), some of the requirements necessary to determine whether a vehicle or a background is contained in the image are not met. For example, in some applications the incorrect classification probability of the vehicle and/or the background, and the correct recognition probability of the vehicle and/or the background need to meet a certain target value, e.g. a target value of zero. In these cases, the classifier often fails to meet the determination requirements and is therefore unable to recognize the vehicle or the background correctly. This holds true not only in cases where the SVM classifier is used to classify and recognize vehicles, but also in cases where the SVM classifier is used to classify and recognize objects such as pedestrians.
In fact, if a fixed confidence value is used as the classification boundary and an incorrect recognition occurs during the classification and recognition of an object, it is difficult for any object classifier using the confidence value as the classification basis to make a correct determination as to whether the object or the background is contained in the image.